| import subprocess | |
| import sys | |
| def install(package): | |
| subprocess.check_call([sys.executable, "-m", "pip", "install", package], stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL) | |
| install("keybert") | |
| install("keyphrase_vectorizers") | |
| import warnings | |
| from keybert import KeyBERT | |
| from keyphrase_vectorizers import KeyphraseCountVectorizer | |
| import gradio as gr | |
| with warnings.catch_warnings(): | |
| warnings.simplefilter("ignore", category=UserWarning) | |
| embedding = 'all-mpnet-base-v2' | |
| key_model = KeyBERT(model=embedding) | |
| vectorizer_params = KeyphraseCountVectorizer(spacy_pipeline='en_core_web_sm', pos_pattern='<J.*>*<N.*>+', stop_words='english', lowercase=True) | |
| def get_keywords(course_name, course_desc): | |
| keywords_list = [] | |
| course_name, course_desc = course_name.strip().lower(), course_desc.strip().lower() | |
| data = course_name+". "+course_desc | |
| keywords = key_model.extract_keywords(data, vectorizer=vectorizer_params, stop_words='english', top_n=7, use_mmr=True) | |
| keywords_list = list(dict(keywords).keys()) | |
| return ", ".join(keywords_list) | |
| iface = gr.Interface(fn=get_keywords, inputs=[gr.Textbox(label="Course Name"), gr.Textbox(label="Course Description")], outputs=gr.Textbox(label="Relevant Tags"), | |
| title="College Course Tags Generator", description="Generating tags/keywords based on Keyphrase-BERT Extraction'") | |
| iface.launch() |